Learning From Demonstration Based on Environmental Constraints

We present a novel learning from demonstration approach which uses environmental constraints as the underlying representation to interpret and reproduce demonstrations. This representation based on environmental constraints separates the information that facilitates generalization from the information specific to object instances. Combined with adaptive controllers which fill in the instance-specific details during execution through explorative interaction, our approach generalizes from a single demonstration on an articulated object to different instances of the same object type. We test our approach in real-world experiments on contact-rich manipulation, using a series of mechanical locks as well as drawers and doors. The high success rate of 95% across all of these experiments provides strong evidence that environmental constraints are a powerful inductive bias for general and robust learning from demonstration.